Your role
As a Sr. SDET in Agentic QA, you will own the test automation and quality frameworks that support Dialpad’s AI Voice Agent services. You will develop automated tests for end-to-end product experiences, from frontend UI to backend services to APIs to audio/text interactions. You will test orchestration flows, agent configuration experiences, and guardian safeguards to create robust automated coverage for functionality, performance, reliability, UX, and more. 

In this role, you will develop substantial amounts of automated test infrastructure and partner deeply with the development team to make our fast-growing AI platform more testable, more stable, and more delightful for customers.

This position is based at one of Dialpad’s Canadian offices and reports to a QA Eng Manager in the United States.

What you’ll do

• Own end-to-end quality for agentic features and workflows, including strategy, development, execution, and release qualification.
• Design and build automation tooling and frameworks for AI/LLM-driven systems, including prompt flows, agent orchestration, and tool integrations.
• Develop and maintain evaluation frameworks (evals) to measure response quality, accuracy, and hallucination rates.
• Drive automation coverage (80%+ for critical AI workflows) using deterministic + probabilistic validation approaches.
• Integrate AI quality checks into CI/CD pipelines with fast feedback cycles (<15 minutes for PR validation).
• Build tooling for LLM observability and debugging, including prompt tracing and response analysis.
• Partner with Applied AI teams on prompt engineering, model selection, and evaluation strategies.
• Design and execute performance and load tests for AI services (latency, throughput, cost efficiency).
• Identify and mitigate risks related to hallucinations, bias, safety, and edge cases.
• Define and track AI quality KPIs (task success rates, precision/recall, latency, etc.).
• Participate in design and architecture reviews to ensure systems are testable, observable, and resilient.
• Mentor engineers and contribute to raising the bar on AI quality engineering practices.

What you’ll bring

• 5+ years of experience in software engineering or SDET roles with an emphasis on software development.
• Strong programming skills in Python (preferred), Java, or JavaScript.
• Experience testing distributed, cloud-native SaaS systems and APIs.
• Demonstrated proficiency in coding with AI agents to accelerate development and improve code quality.
• Hands-on exposure to LLMs or AI/ML systems (e.g., OpenAI, Claude, Gemini, or similar platforms).
• Understanding of non-deterministic systems and probabilistic testing approaches.
• Experience building test frameworks and scalable automation systems.
• Familiarity with AI evaluation techniques (benchmarking, golden datasets, human-in-the-loop validation).
• Experience with CI/CD pipelines (e.g., Jenkins, GitHub Actions).
• Strong collaboration skills with the ability to work across distributed teams and time zones.
• Bachelor’s degree in Computer Science or equivalent practical experience.
• Backend: Python, Go, Google Cloud Platform, Cloud Run / App Engine, Kubernetes, Datastore, Redis, ElasticSearch.
• Frontend: Vue3, React.
• AI Stack: LLM APIs, LiveKit, prompt orchestration frameworks, evaluation tooling.

Your role
As a Sr. SDET in Agentic QA, you will own the test automation and quality frameworks that support Dialpad’s AI Voice Agent services. You will develop automated tests for end-to-end product experiences, from frontend UI to backend services to APIs to audio/text interactions. You will test orchestration flows, agent configuration experiences, and guardian safeguards to create robust automated coverage for functionality, performance, reliability, UX, and more. 

In this role, you will develop substantial amounts of automated test infrastructure and partner deeply with the development team to make our fast-growing AI platform more testable, more stable, and more delightful for customers.

This position is based at one of Dialpad’s Canadian offices and reports to a QA Eng Manager in the United States.

What you’ll do

• Own end-to-end quality for agentic features and workflows, including strategy, development, execution, and release qualification.
• Design and build automation tooling and frameworks for AI/LLM-driven systems, including prompt flows, agent orchestration, and tool integrations.
• Develop and maintain evaluation frameworks (evals) to measure response quality, accuracy, and hallucination rates.
• Drive automation coverage (80%+ for critical AI workflows) using deterministic + probabilistic validation approaches.
• Integrate AI quality checks into CI/CD pipelines with fast feedback cycles (<15 minutes for PR validation).
• Build tooling for LLM observability and debugging, including prompt tracing and response analysis.
• Partner with Applied AI teams on prompt engineering, model selection, and evaluation strategies.
• Design and execute performance and load tests for AI services (latency, throughput, cost efficiency).
• Identify and mitigate risks related to hallucinations, bias, safety, and edge cases.
• Define and track AI quality KPIs (task success rates, precision/recall, latency, etc.).
• Participate in design and architecture reviews to ensure systems are testable, observable, and resilient.
• Mentor engineers and contribute to raising the bar on AI quality engineering practices.

What you’ll bring

• 5+ years of experience in software engineering or SDET roles with an emphasis on software development.
• Strong programming skills in Python (preferred), Java, or JavaScript.
• Experience testing distributed, cloud-native SaaS systems and APIs.
• Demonstrated proficiency in coding with AI agents to accelerate development and improve code quality.
• Hands-on exposure to LLMs or AI/ML systems (e.g., OpenAI, Claude, Gemini, or similar platforms).
• Understanding of non-deterministic systems and probabilistic testing approaches.
• Experience building test frameworks and scalable automation systems.
• Familiarity with AI evaluation techniques (benchmarking, golden datasets, human-in-the-loop validation).
• Experience with CI/CD pipelines (e.g., Jenkins, GitHub Actions).
• Strong collaboration skills with the ability to work across distributed teams and time zones.
• Bachelor’s degree in Computer Science or equivalent practical experience.
• Backend: Python, Go, Google Cloud Platform, Cloud Run / App Engine, Kubernetes, Datastore, Redis, ElasticSearch.
• Frontend: Vue3, React.
• AI Stack: LLM APIs, LiveKit, prompt orchestration frameworks, evaluation tooling.

About this position:

We’re looking for a Machine Learning Infrastructure Engineer to join our growing Data & Platform team and build the foundation that powers our AI and machine learning capabilities across Later’s product portfolio. As our first dedicated ML Infrastructure Engineer, you will own the systems that support model experimentation, training, deployment, and monitoring at scale.

This role is critical to accelerating our data science initiatives and enabling future AI innovation. You’ll design and operate reliable, secure, and scalable ML infrastructure that empowers data scientists and engineers to ship high-impact models with confidence. If you’re excited about building robust ML systems in a fast-moving environment—and want to define the standard for ML Ops at Later—this is your opportunity.

What you'll be doing:

Strategy

• Define and own the long-term ML infrastructure roadmap, ensuring it supports both current experimentation needs and future AI initiatives.
• Establish best practices for model lifecycle management, deployment standards, monitoring, and governance. 
• Identify infrastructure gaps and proactively design scalable solutions to enable high-velocity ML development.
• Contribute to cross-functional technical planning, ensuring ML systems align with product and platform strategy.

Technical/ Execution

• Design, build, and maintain production-grade model deployment and inference systems using CI/CD pipelines, containerized services (Docker), and API frameworks (e.g., Flask).
• Automate end-to-end ML lifecycle workflows including training pipelines, model validation, registry management, deployment, and rollback strategies.
• Implement robust monitoring systems for model performance, latency, drift detection, and infrastructure health using tools such as CloudWatch, Prometheus, and Grafana.
• Operate across AWS and GCP environments to manage training and inference workloads, including GPU-based infrastructure and BigQuery datasets.
• Develop and maintain infrastructure-as-code (Terraform, CloudFormation) to ensure scalable, repeatable, and secure cloud environments.
• Implement and optimize CI/CD workflows (e.g., GitHub Actions, GitLab CI, Bitbucket Pipelines) for ML and infrastructure automation.

Team / Collaboration

• Partner closely with Data Scientists, Analysts, Platform Engineers, and Product Engineers to support end-to-end ML workflows.
• Translate data science experimentation needs into production-ready infrastructure solutions.
• Serve as the technical bridge between ML experimentation and productized deployment.
• Share knowledge and best practices to elevate ML maturity across teams.

Research/Best Practices

• Stay current on emerging ML Ops practices, tools, and frameworks to continuously improve system reliability and efficiency.
• Evaluate and implement model-serving frameworks (e.g., TorchServe, Seldon, TensorRT) where appropriate.
• Contribute to governance, reproducibility, and auditability standards for ML systems.
• Experiment with new tooling and workflows to improve reproducibility, performance, and developer velocity.

What success looks like:

• ML models move from experimentation to production quickly and reliably, with minimal manual intervention.
• CI/CD pipelines enable safe, repeatable deployments with clear rollback strategies.
• Model performance, drift, and infrastructure health are proactively monitored and observable.
• Infrastructure supports scalable GPU training and real-time inference without bottlenecks.
• Data scientists report improved velocity, reproducibility, and confidence in deploying models.
• ML systems are secure, compliant, and aligned with evolving product and AI strategy.

What you bring:

• 4+ years of experience in ML Ops, ML infrastructure, backend engineering, or related roles supporting production ML systems.
• Experience working in cloud-native environments (AWS and/or GCP) with hands-on deployment of ML workloads.
• Proven track record designing and implementing CI/CD pipelines for ML systems.
• Strong experience with Amazon SageMaker, Docker, Flask-based APIs, and infrastructure automation tools.
• Hands-on experience with ML lifecycle tooling such as MLflow, SageMaker Studio, or Weights & Biases.
• Experience managing container orchestration platforms (Kubernetes, EKS, or GKE).
• Strong programming experience in Python (additional experience in Go, Java, or Scala is a plus).
• Experience working with infrastructure-as-code tools such as Terraform or CloudFormation.
• Familiarity with observability tools such as CloudWatch, Prometheus, Grafana, Datadog, or centralized logging platforms.
• Experience managing GPU-based workloads and scaling training/inference systems.
• Familiarity with data infrastructure tools such as BigQuery and cloud-native data pipelines.
• Bonus: Experience supporting LLMs or generative AI pipelines, distributed training systems, feature stores (e.g., Feast), real-time inference systems, or ML governance frameworks.
• A mindset focused on automation, reliability, performance, and continuous improvement in fast-scaling environments.

How you work: 

• Driven by Impact: You deliver results that matter—prioritizing high-value work, meeting deadlines, and adapting quickly while keeping outcomes clear.
• Strategic & Customer-Centric: You anticipate risks and opportunities, connect decisions to long-term growth, and build trust through proactive insights.
• Curious & Growth-Oriented: You seek knowledge, ask sharp questions, and apply learnings fast—challenging the status quo with a mindset of improvement.
• Collaborative & Resilient: You thrive in change by staying resourceful, solution-focused, and positive—removing roadblocks, sharing insights, and keeping morale high.
• Accountable & Honest: You own your work, hold yourself and others to a high bar, and use transparent feedback to drive growth.
• Emotionally Intelligent: You build trust through empathy and collaboration, foster inclusion, and inspire others with grit, optimism, and integrity.

Our approach to compensation:

We take a market-based & data-driven approach to compensation. We leverage data from trusted third-party compensation sources to help us understand the market value of a role based on function, level, geographic location, and scope. We evaluate compensation bi-annually, including performance and market-related factors.

Our salaries are benchmarked against market Total Cash Compensation for the geographic location of our job posting. Compensation for some roles is structured as On Target Earnings (OTE = base + commission/variable) while for others it is structured as Salary only.

To comply with local legislation and ensure transparency, we share salary ranges on all job postings. Skills, experience and other factors help determine the final salary we offer which may vary from the original range posted. 

Additionally, all permanent team members are eligible to participate in various benefits plans as part of their overall compensation package.

Salary Range: 

$ 145,000 -165,000 

#LI-remote

About this position:

We’re looking for a Machine Learning Infrastructure Engineer to join our growing Data & Platform team and build the foundation that powers our AI and machine learning capabilities across Later’s product portfolio. As our first dedicated ML Infrastructure Engineer, you will own the systems that support model experimentation, training, deployment, and monitoring at scale.

This role is critical to accelerating our data science initiatives and enabling future AI innovation. You’ll design and operate reliable, secure, and scalable ML infrastructure that empowers data scientists and engineers to ship high-impact models with confidence. If you’re excited about building robust ML systems in a fast-moving environment—and want to define the standard for ML Ops at Later—this is your opportunity.

What you'll be doing:

Strategy

• Define and own the long-term ML infrastructure roadmap, ensuring it supports both current experimentation needs and future AI initiatives.
• Establish best practices for model lifecycle management, deployment standards, monitoring, and governance. 
• Identify infrastructure gaps and proactively design scalable solutions to enable high-velocity ML development.
• Contribute to cross-functional technical planning, ensuring ML systems align with product and platform strategy.

Technical/ Execution

• Design, build, and maintain production-grade model deployment and inference systems using CI/CD pipelines, containerized services (Docker), and API frameworks (e.g., Flask).
• Automate end-to-end ML lifecycle workflows including training pipelines, model validation, registry management, deployment, and rollback strategies.
• Implement robust monitoring systems for model performance, latency, drift detection, and infrastructure health using tools such as CloudWatch, Prometheus, and Grafana.
• Operate across AWS and GCP environments to manage training and inference workloads, including GPU-based infrastructure and BigQuery datasets.
• Develop and maintain infrastructure-as-code (Terraform, CloudFormation) to ensure scalable, repeatable, and secure cloud environments.
• Implement and optimize CI/CD workflows (e.g., GitHub Actions, GitLab CI, Bitbucket Pipelines) for ML and infrastructure automation.

Team / Collaboration

• Partner closely with Data Scientists, Analysts, Platform Engineers, and Product Engineers to support end-to-end ML workflows.
• Translate data science experimentation needs into production-ready infrastructure solutions.
• Serve as the technical bridge between ML experimentation and productized deployment.
• Share knowledge and best practices to elevate ML maturity across teams.

Research/Best Practices

• Stay current on emerging ML Ops practices, tools, and frameworks to continuously improve system reliability and efficiency.
• Evaluate and implement model-serving frameworks (e.g., TorchServe, Seldon, TensorRT) where appropriate.
• Contribute to governance, reproducibility, and auditability standards for ML systems.
• Experiment with new tooling and workflows to improve reproducibility, performance, and developer velocity.

What success looks like:

• ML models move from experimentation to production quickly and reliably, with minimal manual intervention.
• CI/CD pipelines enable safe, repeatable deployments with clear rollback strategies.
• Model performance, drift, and infrastructure health are proactively monitored and observable.
• Infrastructure supports scalable GPU training and real-time inference without bottlenecks.
• Data scientists report improved velocity, reproducibility, and confidence in deploying models.
• ML systems are secure, compliant, and aligned with evolving product and AI strategy.

What you bring:

• 4+ years of experience in ML Ops, ML infrastructure, backend engineering, or related roles supporting production ML systems.
• Experience working in cloud-native environments (AWS and/or GCP) with hands-on deployment of ML workloads.
• Proven track record designing and implementing CI/CD pipelines for ML systems.
• Strong experience with Amazon SageMaker, Docker, Flask-based APIs, and infrastructure automation tools.
• Hands-on experience with ML lifecycle tooling such as MLflow, SageMaker Studio, or Weights & Biases.
• Experience managing container orchestration platforms (Kubernetes, EKS, or GKE).
• Strong programming experience in Python (additional experience in Go, Java, or Scala is a plus).
• Experience working with infrastructure-as-code tools such as Terraform or CloudFormation.
• Familiarity with observability tools such as CloudWatch, Prometheus, Grafana, Datadog, or centralized logging platforms.
• Experience managing GPU-based workloads and scaling training/inference systems.
• Familiarity with data infrastructure tools such as BigQuery and cloud-native data pipelines.
• Bonus: Experience supporting LLMs or generative AI pipelines, distributed training systems, feature stores (e.g., Feast), real-time inference systems, or ML governance frameworks.
• A mindset focused on automation, reliability, performance, and continuous improvement in fast-scaling environments.

How you work: 

• Driven by Impact: You deliver results that matter—prioritizing high-value work, meeting deadlines, and adapting quickly while keeping outcomes clear.
• Strategic & Customer-Centric: You anticipate risks and opportunities, connect decisions to long-term growth, and build trust through proactive insights.
• Curious & Growth-Oriented: You seek knowledge, ask sharp questions, and apply learnings fast—challenging the status quo with a mindset of improvement.
• Collaborative & Resilient: You thrive in change by staying resourceful, solution-focused, and positive—removing roadblocks, sharing insights, and keeping morale high.
• Accountable & Honest: You own your work, hold yourself and others to a high bar, and use transparent feedback to drive growth.
• Emotionally Intelligent: You build trust through empathy and collaboration, foster inclusion, and inspire others with grit, optimism, and integrity.

Our approach to compensation:

We take a market-based & data-driven approach to compensation. We leverage data from trusted third-party compensation sources to help us understand the market value of a role based on function, level, geographic location, and scope. We evaluate compensation bi-annually, including performance and market-related factors.

Our salaries are benchmarked against market Total Cash Compensation for the geographic location of our job posting. Compensation for some roles is structured as On Target Earnings (OTE = base + commission/variable) while for others it is structured as Salary only.

To comply with local legislation and ensure transparency, we share salary ranges on all job postings. Skills, experience and other factors help determine the final salary we offer which may vary from the original range posted. 

Additionally, all permanent team members are eligible to participate in various benefits plans as part of their overall compensation package.

Salary Range: 

$ 145,000 -165,000 

#LI-remote

About this position:

We’re looking for a Machine Learning Infrastructure Engineer to join our growing Data & Platform team and build the foundation that powers our AI and machine learning capabilities across Later’s product portfolio. As our first dedicated ML Infrastructure Engineer, you will own the systems that support model experimentation, training, deployment, and monitoring at scale.

This role is critical to accelerating our data science initiatives and enabling future AI innovation. You’ll design and operate reliable, secure, and scalable ML infrastructure that empowers data scientists and engineers to ship high-impact models with confidence. If you’re excited about building robust ML systems in a fast-moving environment—and want to define the standard for ML Ops at Later—this is your opportunity.

What you'll be doing:

Strategy

• Define and own the long-term ML infrastructure roadmap, ensuring it supports both current experimentation needs and future AI initiatives.
• Establish best practices for model lifecycle management, deployment standards, monitoring, and governance. 
• Identify infrastructure gaps and proactively design scalable solutions to enable high-velocity ML development.
• Contribute to cross-functional technical planning, ensuring ML systems align with product and platform strategy.

Technical/ Execution

• Design, build, and maintain production-grade model deployment and inference systems using CI/CD pipelines, containerized services (Docker), and API frameworks (e.g., Flask).
• Automate end-to-end ML lifecycle workflows including training pipelines, model validation, registry management, deployment, and rollback strategies.
• Implement robust monitoring systems for model performance, latency, drift detection, and infrastructure health using tools such as CloudWatch, Prometheus, and Grafana.
• Operate across AWS and GCP environments to manage training and inference workloads, including GPU-based infrastructure and BigQuery datasets.
• Develop and maintain infrastructure-as-code (Terraform, CloudFormation) to ensure scalable, repeatable, and secure cloud environments.
• Implement and optimize CI/CD workflows (e.g., GitHub Actions, GitLab CI, Bitbucket Pipelines) for ML and infrastructure automation.

Team / Collaboration

• Partner closely with Data Scientists, Analysts, Platform Engineers, and Product Engineers to support end-to-end ML workflows.
• Translate data science experimentation needs into production-ready infrastructure solutions.
• Serve as the technical bridge between ML experimentation and productized deployment.
• Share knowledge and best practices to elevate ML maturity across teams.

Research/Best Practices

• Stay current on emerging ML Ops practices, tools, and frameworks to continuously improve system reliability and efficiency.
• Evaluate and implement model-serving frameworks (e.g., TorchServe, Seldon, TensorRT) where appropriate.
• Contribute to governance, reproducibility, and auditability standards for ML systems.
• Experiment with new tooling and workflows to improve reproducibility, performance, and developer velocity.

What success looks like:

• ML models move from experimentation to production quickly and reliably, with minimal manual intervention.
• CI/CD pipelines enable safe, repeatable deployments with clear rollback strategies.
• Model performance, drift, and infrastructure health are proactively monitored and observable.
• Infrastructure supports scalable GPU training and real-time inference without bottlenecks.
• Data scientists report improved velocity, reproducibility, and confidence in deploying models.
• ML systems are secure, compliant, and aligned with evolving product and AI strategy.

What you bring:

• 4+ years of experience in ML Ops, ML infrastructure, backend engineering, or related roles supporting production ML systems.
• Experience working in cloud-native environments (AWS and/or GCP) with hands-on deployment of ML workloads.
• Proven track record designing and implementing CI/CD pipelines for ML systems.
• Strong experience with Amazon SageMaker, Docker, Flask-based APIs, and infrastructure automation tools.
• Hands-on experience with ML lifecycle tooling such as MLflow, SageMaker Studio, or Weights & Biases.
• Experience managing container orchestration platforms (Kubernetes, EKS, or GKE).
• Strong programming experience in Python (additional experience in Go, Java, or Scala is a plus).
• Experience working with infrastructure-as-code tools such as Terraform or CloudFormation.
• Familiarity with observability tools such as CloudWatch, Prometheus, Grafana, Datadog, or centralized logging platforms.
• Experience managing GPU-based workloads and scaling training/inference systems.
• Familiarity with data infrastructure tools such as BigQuery and cloud-native data pipelines.
• Bonus: Experience supporting LLMs or generative AI pipelines, distributed training systems, feature stores (e.g., Feast), real-time inference systems, or ML governance frameworks.
• A mindset focused on automation, reliability, performance, and continuous improvement in fast-scaling environments.

How you work: 

• Driven by Impact: You deliver results that matter—prioritizing high-value work, meeting deadlines, and adapting quickly while keeping outcomes clear.
• Strategic & Customer-Centric: You anticipate risks and opportunities, connect decisions to long-term growth, and build trust through proactive insights.
• Curious & Growth-Oriented: You seek knowledge, ask sharp questions, and apply learnings fast—challenging the status quo with a mindset of improvement.
• Collaborative & Resilient: You thrive in change by staying resourceful, solution-focused, and positive—removing roadblocks, sharing insights, and keeping morale high.
• Accountable & Honest: You own your work, hold yourself and others to a high bar, and use transparent feedback to drive growth.
• Emotionally Intelligent: You build trust through empathy and collaboration, foster inclusion, and inspire others with grit, optimism, and integrity.

Our approach to compensation:

We take a market-based & data-driven approach to compensation. We leverage data from trusted third-party compensation sources to help us understand the market value of a role based on function, level, geographic location, and scope. We evaluate compensation bi-annually, including performance and market-related factors.

Our salaries are benchmarked against market Total Cash Compensation for the geographic location of our job posting. Compensation for some roles is structured as On Target Earnings (OTE = base + commission/variable) while for others it is structured as Salary only.

To comply with local legislation and ensure transparency, we share salary ranges on all job postings. Skills, experience and other factors help determine the final salary we offer which may vary from the original range posted. 

Additionally, all permanent team members are eligible to participate in various benefits plans as part of their overall compensation package.

Salary Range: 

$ 145,000 -165,000 

#LI-remote

High-Level Overview

Benevity is seeking a talented Senior Staff Developer, who has an extensive record of hands-on Full Stack Development and Architecture experience. This role, reporting to the Director of Engineering, plays a crucial part in shaping and executing on the technical strategy across different teams and layers of the technology organization.

What you'll do:

Mixture of leadership and technical background

• Full stack solution design expert for technology initiatives using public cloud platforms  spanning multiple teams and domains within the organization.
• Demonstrated experience in one or more of the following domains: 
• Data processing & lifecycle management
• Multi-region data distribution and compliance management
• Data quality, observability, audit
• Content & digital asset management
• Personalized search solution and indexing strategy
• Domain driven design patterns
• Ensure security best practice across the technology stack using cloud native solution design.
• Create and maintain technical documentation related to architecture, decisions record, and implementation processes.
• Drive higher performance, agility and time to market by promoting the adoption of industry standard tools and processes across teams.
• Ideate and implement Proof of Concepts for technical and product advancement.
• Work with Principal Architects to lead the technical direction of our organizational strategy through implementing robust, extendable, and reusable architecture patterns.
• Provide mentorship and guidance to senior and staff developers, encouraging continuous learning and professional development through practical, hands-on examples and constructive feedback, and fostering a collaborative and inclusive team environment.
• Collaborate with product managers, staff developers, and other stakeholders to translate business requirements into technical specifications.

What you'll bring:

• 8+ years of scalable  development experience, with lead and software design accountabilities.
• 2+ years in a leadership role on building scalable platforms, technology transformation and modernization initiatives.
• Solid understanding of software architecture, design patterns, and principles
• The ability to connect designs to implementation with an API-first approach expertise with loosely coupled systems, and domain driven design
• Experience with event-driven architecture and decoupled services
• Proficiency in agile methodologies, Infrastructure-as-Code, DevSecOps best practices, and automated test frameworks
• Comfort with abstract problem-solving and ambiguous challenges
• Well versed in adapter architecture and integration design patterns
• AI driven development with ethical and secured software delivery practice
• Proven track record of building performant, scalable and cost effective products
• Proficiency in Object-Oriented programming methodologies, particularly using Java and Python programming languages. 
• Experience with application frameworks such as Spring Boot and FastAPI
• Strong experience domain driven database design to manage datasets using relational, document and warehouse database engines
• Ability to improve velocity and impact by eliminating technical debt.
• Hands-on experience with designing and implementing enterprise technology and data engineering solutions
• Well versed in integration patterns.
• Extensive experience with cloud-native infrastructure in AWS and GCP cloud platforms. Familiarity with Azure is a plus.
• Experience building scalable and automated software delivery supply chain using CI/CD concepts using technologies such as GitHub Actions and Jenkins 
• Experience in configuration driven data engineering knowledge developing scalable and extensible ETL and ELT based data processing.
• Commitment to continuous improvement in code, processes, and team development
• Excellent communication skills, translating user needs into actionable technical plans
• Degree in Computer Science, Computer Engineering or equivalent professional experience

Lantern is seeking a Senior Software Engineer to join our healthcare technology team and help shape the future of our platform. In this role, you will drive technical innovation, design and build robust Python-based microservices, and maintain existing C# .NET applications as we transition toward a modern, scalable architecture. You’ll work across the full stack—building secure APIs, developing intuitive frontends, and delivering cloud-native solutions that improve the lives of patients and healthcare providers.

The ideal candidate is a hands-on engineer and technical leader who promotes a culture of knowledge sharing, collaboration, and continuous improvement. You’re comfortable working across domains, partnering with Product, and other stakeholders, and navigating ambiguity to make pragmatic architectural decisions. We value engineers who take ownership, communicate clearly, and are eager to mentor others while raising the technical bar.

At Lantern, we thrive on solving complex challenges and reimagining how healthcare is delivered. We’re looking for someone who brings creativity, curiosity, and a passion for building scalable, maintainable systems—along with a strong sense of teamwork, delivery focus, and a genuine enthusiasm for improving healthcare through technology.

Location: Hybrid - at least 3 days/wk in Vancouver, BC

Responsibilities:

• Design and implement robust backend solutions using Python (FastAPI, Django, or Flask) and contribute to .NET (C#) systems during the ongoing platform transition.
• Develop, document, and maintain secure RESTful APIs for integration with internal applications, third-party services, and mobile platforms.
• Build and maintain responsive frontends using React or Angular, tailored for care advocates and patient-facing applications.
• Leverage cloud platforms (Azure, AWS) to develop scalable, high-availability solutions using cloud-native services such as functions, messaging queues, storage, and managed databases.
• Maintain CI/CD pipelines in co-ownership with infrastructure team to support continuous delivery and infrastructure automation with tools like Azure DevOps or GitHub Actions.
• Collaborate closely with Product, and QA teams to define technical requirements and ensure regulatory alignment.
• Participate in system architecture design, peer code reviews, and sprint planning to drive high-quality engineering outcomes.
• Mentor junior developers and contribute to internal technical standards, documentation, and best practices.

Requirements:

• Bachelor’s degree in Computer Science, Engineering, or equivalent experience
• A minimum of 5 years of experience in software development, with strong proficiency in Python and working knowledge of .NET (C#) for maintaining legacy systems.
• A minimum of 5 years of experience building secure, scalable REST APIs and backend services.
• Solid understanding of relational databases such as Microsoft SQL, PostgreSQL, or MySQL.
• Experience writing unit and integration tests using frameworks such as PyTest, xUnit, or Jest to ensure code quality and maintainability.
• Familiarity with OAuth 2.0 authentication and authorization flows.
• Strong experience with cloud platforms like Azure or AWS, including serverless and containerized services.
• Excellent problem-solving and analytical skills, with a proactive approach to modern design and development practices.
• Familiarity with healthcare data standards (FHIR, HL7, X12) and HIPAA-compliant environments is a plus
• Experience working within Agile/Scrum methodologies.

Benefits:

• Medical Insurance
• Dental Insurance
• Vision Insurance
• Short- & Long-Term Disability
• Life Insurance
• Flexible PTO
• RRSP with company match

Lantern is seeking a Senior Software Engineer to join our healthcare technology team and help shape the future of our platform. In this role, you will drive technical innovation, design and build robust Python-based microservices, and maintain existing C# .NET applications as we transition toward a modern, scalable architecture. You’ll work across the full stack—building secure APIs, developing intuitive frontends, and delivering cloud-native solutions that improve the lives of patients and healthcare providers.

The ideal candidate is a hands-on engineer and technical leader who promotes a culture of knowledge sharing, collaboration, and continuous improvement. You’re comfortable working across domains, partnering with Product, and other stakeholders, and navigating ambiguity to make pragmatic architectural decisions. We value engineers who take ownership, communicate clearly, and are eager to mentor others while raising the technical bar.

At Lantern, we thrive on solving complex challenges and reimagining how healthcare is delivered. We’re looking for someone who brings creativity, curiosity, and a passion for building scalable, maintainable systems—along with a strong sense of teamwork, delivery focus, and a genuine enthusiasm for improving healthcare through technology.

Location: Hybrid - at least 3 days/wk in one of our offices:  Dallas, TX (preferred) / Chicago/Evanston / New York / Washington, DC

Responsibilities:

• Design and implement robust backend solutions using Python (FastAPI, Django, or Flask) and contribute to .NET (C#) systems during the ongoing platform transition.
• Develop, document, and maintain secure RESTful APIs for integration with internal applications, third-party services, and mobile platforms.
• Build and maintain responsive frontends using React or Angular, tailored for care advocates and patient-facing applications.
• Leverage cloud platforms (Azure, AWS) to develop scalable, high-availability solutions using cloud-native services such as functions, messaging queues, storage, and managed databases.
• Maintain CI/CD pipelines in co-ownership with infrastructure team to support continuous delivery and infrastructure automation with tools like Azure DevOps or GitHub Actions.
• Collaborate closely with Product, and QA teams to define technical requirements and ensure regulatory alignment.
• Participate in system architecture design, peer code reviews, and sprint planning to drive high-quality engineering outcomes.
• Mentor junior developers and contribute to internal technical standards, documentation, and best practices.

Requirements:

• Bachelor’s degree in Computer Science, Engineering, or equivalent experience
• A minimum of 5 years of experience in software development, with strong proficiency in Python and working knowledge of .NET (C#) for maintaining legacy systems.
• A minimum of 5 years of experience building secure, scalable REST APIs and backend services.
• Solid understanding of relational databases such as Microsoft SQL, PostgreSQL, or MySQL.
• Experience writing unit and integration tests using frameworks such as PyTest, xUnit, or Jest to ensure code quality and maintainability.
• Familiarity with OAuth 2.0 authentication and authorization flows.
• Strong experience with cloud platforms like Azure or AWS, including serverless and containerized services.
• Excellent problem-solving and analytical skills, with a proactive approach to modern design and development practices.
• Familiarity with healthcare data standards (FHIR, HL7, X12) and HIPAA-compliant environments is a plus
• Experience working within Agile/Scrum methodologies.

Benefits

• Medical Insurance
• Dental Insurance
• Vision Insurance
• Short & Long Term Disability
• Life Insurance
• 401k with company match
• Paid Time Off
• Paid Parental Leave

Lantern is seeking an experienced Senior Cloud Platform Engineer to drive the evolution of our Azure-based infrastructure, focusing on scalability, automation, and regulatory compliance. In this pivotal role, you will architect, implement, and operate a secure, resilient, multi-region cloud platform that supports our mission to make excellent, affordable care more accessible to everyone.

You will collaborate with a talented team of cloud engineers and architects, championing operational excellence and delivering highly available, HIPAA- and SOC-compliant infrastructure. Working closely with security, application development, and business teams, you will help build robust, enterprise-grade platforms that empower innovation, agility, and growth.

This role is ideal for a cloud expert with deep Microsoft Azure expertise, a strong record of contributing to high-performing teams, and a passion for building scalable infrastructure in dynamic, fast-paced enterprise environments.

Location: Hybrid - at least 3 days/wk in Vancouver, BC
On-Call:  This position requires being on-call 1 week per month at the most

Responsibilities:

• Design, implement, and maintain secure, scalable, and highly available Azure cloud infrastructure to support Lantern’s healthcare solutions.
• Collaborate with cloud architects, engineers, application developers, and InfoSec teams to deliver enterprise-grade cloud platforms that meet HIPAA, SOC 2, and other regulatory requirements.
• Develop and maintain Infrastructure-as-Code (IaC) using tools such as Terraform to automate provisioning, configuration, and deployment of Azure resources.
• Implement and optimize CI/CD pipelines using Azure DevOps and GitHub Actions to support agile software development and rapid release cycles.
• Ensure robust monitoring, observability, and alerting for production systems; proactively address incidents and contribute to incident response.
• Participate in cost optimization efforts by monitoring resource usage and recommending efficiencies for cloud spend.
• Contribute to disaster recovery planning, business continuity, and uptime strategies for critical healthcare applications.
• Maintain comprehensive documentation for infrastructure, deployments, and operational procedures.
• Participate in cross-functional meetings with product, engineering, and compliance teams to ensure infrastructure aligns with business and regulatory goals.
• Mentor junior engineers and support a culture of collaboration and continuous improvement within the team.

 Requirements:

• Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field, or equivalent practical experience.
• 4+ years of experience in cloud infrastructure engineering, systems administration, or DevOps roles.
• 3+ years of hands-on experience with Microsoft Azure (experience with AWS or Google Cloud Platform is a plus).
• Experience designing, deploying, and managing cloud-based infrastructure at scale.
• Practical experience configuring and maintaining CI/CD pipelines using Azure DevOps and GitHub Actions.
• Proven experience in a regulated environment, ideally in the healthcare sector with HIPAA and/or SOC compliance requirements.
• Hands-on exposure to security best practices for cloud environments, including identity and access management, encryption, and monitoring/logging.
• Strong problem-solving and troubleshooting abilities.
• Excellent communication and collaboration skills.
• If you don’t meet every requirement listed, we still encourage you to apply.

Strong Candidates Will: 

• Strong experience with Infrastructure as Code (IaC), especially Terraform, to automate Azure resources.
• Experience managing Azure App Services, networking, and storage solutions.
• Experience with containerization (Docker, Azure Kubernetes Service) is a plus.
• Relevant Azure certifications (e.g., Azure Solutions Architect, Azure Administrator) are a plus.

Benefits: 

• Medical Insurance
• Dental Insurance
• Vision Insurance
• Short- & Long-Term Disability
• Life Insurance
• RRSP with company match

About Your Role

As a Senior TestOps Engineer within our Delivery Engineering team, you will be the architect of our quality gates. You won't just be "testing" software,  you will be building the high performance infrastructure and automated tooling that allows our developers to ship code with confidence and speed. You will bridge the gap between QA, DevOps, and Software Engineering to ensure our delivery pipeline is resilient, observable, and scalable.

This role is hybrid based in Vancouver

Salary range: CA$125,000 to CA$150,000 + stock options

You Will 

• Architect Test Infrastructure: Design and implement ephemeral test environments and automation frameworks for the various Kubernetes platforms in different Clouds using Golang, Docker/Kubernetes, and Terraform/Pulumi.
• Eliminate Toil: Identify manual bottlenecks in the delivery process and write automated tools and solutions to replace them using Golang.
• Gap Analysis and Strategy: Proactively identify architectural blind spots in complex integration points and write robust automated test suites using Go-test and Ginkgo, ensuring high-concurrency and flake-resistant execution.
• Engineer CI Pipelines: Build and maintain sophisticated CI/CD workflows (GitHub Actions, Tekton) that integrate automated testing seamlessly into the development lifecycle.
• Observability Integration: Work with OpenTelemetry, Elasticsearch, and Kibana to ensure test results provide deep, actionable insights into system health.
• Support Release delivery: Lead the creation of comprehensive release test plans, managing risk communication across Product and Engineering stakeholders.
• Champion Exploratory Testing: Beyond automation, apply a "breaker" mindset to uncover edge cases through exploratory testing and performance benchmarking.

You Have

• Experience: 5+ years in Software Test Tooling, DevOps, or Site Reliability Engineering (SRE).
• The "Engineer’s Engineer": Strong development skills in Go or Python. We need someone who doesn’t just use tools but builds them. Has a deep-seated drive to improve the developer experience by creating seamless, automated guardrails. A wide majority of our tools are built in-house.
• IaC Mindset: Experience treating infrastructure as a software problem (eg. Terraform, Pulumi) and believes environments should be as ephemeral and versioned as the code itself.
• Cloud & Containers: Comfortable navigating the complexities of K8s, from managing container lifecycles in Docker to troubleshooting pod lifecycles and resource usage in AWS/GCP/Azure.
• Quality Strategists: Who don’t just "run tests"; they identify coverage gaps at the architectural level. Have the foresight to pinpoint where service boundaries might fail and possess the skill to automate the validation of those integration points.
• CI/CD Architects: A star designing high performance pipelines (e.g., GitHub Actions, Tekton) that provide fast, reliable feedback loops to engineering teams.
• Observability Advocates: Believes that testing doesn't end at "Pass/Fail." And are looking for ways to integrate OpenTelemetry, Elastic, or Kibana to gain deep insights into system reliability via test performance.

Nice-to-Have

• Platform Mastery: Experience with Kubernetes distributions like OpenShift, Rancher, MKE.
• Hybrid Cloud: Familiarity with virtualization (vSphere, KubeVirt).
• The Logging Stack: Hands-on experience with Log aggregation (ElasticSearch/OpenSearch).
• Observability: Experience with distributed tracing (OpenTelemetry) and complex performance profiling.

Sony Pictures Imageworks is located on the unceded traditional territory of the Musqueam, Squamish, and Tsleil-Waututh First Nations. We are committed to respecting traditional lands, and working with communities towards reconciliation.

Sony Pictures Imageworks Canada Inc.

658 Homer Street., Suite 405, Vancouver, BC, V6B 2R4

Project based, Full Time

Language in work environment - English

Benefits per company policy: include healthcare, tuition reimbursement, RRSP's, Sick and Vacation leave, standard increases as applicable

JOB SUMMARY
We are seeking a skilled DevOps Engineer to join our technology team supporting global content production, post-production, and distribution workflows. The ideal candidate will have a strong background in cloud infrastructure automation, scalable deployment architectures, and Kubernetes cluster management. You will be working closely with our engineering, media operations, and security teams to ensure our platforms can scale dynamically to meet the needs of high-traffic media pipelines and digital distribution systems.

EDUCATION, QUALIFICATIONS AND EXPERIENCE

FORMAL EDUCATION
● Bachelor’s degree, with a preference for Computer Science, Engineering or other technical degree.

PRIMARY AREAS OF RESPONSIBILITY WILL INCLUDE:
● Manage and optimize multi-cluster Kubernetes environments across AWS, GCP, or Azure.
● Implement autoscaling policies and node management strategies for high-availability workloads.

● Maintain service mesh configurations (e.g., Istio or Linkerd) and monitor cluster health.
● Design and maintain infrastructure-as-code (IaC) with Terraform, Helm, or CloudFormation.
● Build and manage automated CI/CD pipelines using GitHub Actions, Jenkins, or GitLab CI.
● Implement and optimize horizontal/vertical scaling for microservices and API workloads.
● Support serverless and hybrid-cloud architectures where applicable.
● Automate routine operational tasks, deployments, and environment provisioning.
● Set up and maintain monitoring, logging, and alerting systems using tools like Zabbix, Grafana, Pagerduty, ELK, or Datadog.
● Continuously improve system reliability and deployment velocity.
● Partner with development teams to containerize applications and ensure production readiness.
● Collaborate with security teams to enforce compliance, patch management, and secrets handling (Vault, AWS KMS, etc.).
● Participate in on-call rotations and post-incident reviews.

EXPERIENCE:
● 3–6 years of experience in DevOps, Site Reliability, or Cloud Engineering roles.
● Strong hands-on experience managing Kubernetes clusters in production.
● Proficiency in one or more public cloud platforms (AWS, GCP, or Azure).
● Experience with IaC tools (Terraform, Helm, or Ansible).
● Strong scripting skills (Python, Bash, or Go).
● Familiarity with containerization (Docker) and CI/CD tooling.
● Experience supporting high-throughput or media-intensive workloads.
● Knowledge of CDN integration, data pipelines, or transcoding workflows.
● Familiarity with cost optimization and cloud performance tuning.
● Exposure to zero-downtime deployment strategies and GitOps practices (ArgoCD, Flux).
● Certifications such as CKA, CKAD, or cloud-specific equivalents.

OTHER:
● You will establish a clear understanding of the company's vision, goals and strategy and actively contribute to the achievement of the goals and execution of the strategy
● You will be a leader in the organization, responsible for both setting the standards for project management and holding the organization accountable to the standards set
● You are results-oriented with a history of consistently meeting deadlines

● You possess a proactive mindset with a focus on continuous improvement
● You possess excellent written and spoken technical English, as well as strong documentation skills
● You are capable of working with distributed teams across software, infrastructure, and content delivery, in a fast-paced, production-oriented environment

LOCATION AND WORK MODEL
● Vancouver / Hybrid — occasional on-site collaboration with studio technology and production teams.

The anticipated base salary for this position is $104,200.00 to $145,000.00/annually. This role may also qualify for annual incentive and/or comprehensive benefits per company policy: including healthcare, tuition reimbursement, RRSP's, sick and vacation leave, standard increases as applicable. The actual base salary offered will depend on a variety of factors, including without limitation, the qualifications of the individual applicant for the position, years of relevant experience, level of education attained, certifications or other professional licenses held, and if applicable, the location of the position.

We value unique perspectives, and want diverse, unique talent to work with us. We encourage candidates from all identities to apply.

Sony Pictures Entertainment is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, national origin, disability, age, sexual orientation, gender identity, or other protected characteristics.

About this position:

We’re looking for a Machine Learning Infrastructure Engineer to join our growing Data & Platform team and build the foundation that powers our AI and machine learning capabilities across Later’s product portfolio. As our first dedicated ML Infrastructure Engineer, you will own the systems that support model experimentation, training, deployment, and monitoring at scale.

This role is critical to accelerating our data science initiatives and enabling future AI innovation. You’ll design and operate reliable, secure, and scalable ML infrastructure that empowers data scientists and engineers to ship high-impact models with confidence. If you’re excited about building robust ML systems in a fast-moving environment—and want to define the standard for ML Ops at Later—this is your opportunity.

What you'll be doing:

Strategy

• Define and own the long-term ML infrastructure roadmap, ensuring it supports both current experimentation needs and future AI initiatives.
• Establish best practices for model lifecycle management, deployment standards, monitoring, and governance. 
• Identify infrastructure gaps and proactively design scalable solutions to enable high-velocity ML development.
• Contribute to cross-functional technical planning, ensuring ML systems align with product and platform strategy.

Technical/ Execution

• Design, build, and maintain production-grade model deployment and inference systems using CI/CD pipelines, containerized services (Docker), and API frameworks (e.g., Flask).
• Automate end-to-end ML lifecycle workflows including training pipelines, model validation, registry management, deployment, and rollback strategies.
• Implement robust monitoring systems for model performance, latency, drift detection, and infrastructure health using tools such as CloudWatch, Prometheus, and Grafana.
• Operate across AWS and GCP environments to manage training and inference workloads, including GPU-based infrastructure and BigQuery datasets.
• Develop and maintain infrastructure-as-code (Terraform, CloudFormation) to ensure scalable, repeatable, and secure cloud environments.
• Implement and optimize CI/CD workflows (e.g., GitHub Actions, GitLab CI, Bitbucket Pipelines) for ML and infrastructure automation.

Team / Collaboration

• Partner closely with Data Scientists, Analysts, Platform Engineers, and Product Engineers to support end-to-end ML workflows.
• Translate data science experimentation needs into production-ready infrastructure solutions.
• Serve as the technical bridge between ML experimentation and productized deployment.
• Share knowledge and best practices to elevate ML maturity across teams.

Research/Best Practices

• Stay current on emerging ML Ops practices, tools, and frameworks to continuously improve system reliability and efficiency.
• Evaluate and implement model-serving frameworks (e.g., TorchServe, Seldon, TensorRT) where appropriate.
• Contribute to governance, reproducibility, and auditability standards for ML systems.
• Experiment with new tooling and workflows to improve reproducibility, performance, and developer velocity.

What success looks like:

• ML models move from experimentation to production quickly and reliably, with minimal manual intervention.
• CI/CD pipelines enable safe, repeatable deployments with clear rollback strategies.
• Model performance, drift, and infrastructure health are proactively monitored and observable.
• Infrastructure supports scalable GPU training and real-time inference without bottlenecks.
• Data scientists report improved velocity, reproducibility, and confidence in deploying models.
• ML systems are secure, compliant, and aligned with evolving product and AI strategy.

What you bring:

• 4+ years of experience in ML Ops, ML infrastructure, backend engineering, or related roles supporting production ML systems.
• Experience working in cloud-native environments (AWS and/or GCP) with hands-on deployment of ML workloads.
• Proven track record designing and implementing CI/CD pipelines for ML systems.
• Strong experience with Amazon SageMaker, Docker, Flask-based APIs, and infrastructure automation tools.
• Hands-on experience with ML lifecycle tooling such as MLflow, SageMaker Studio, or Weights & Biases.
• Experience managing container orchestration platforms (Kubernetes, EKS, or GKE).
• Strong programming experience in Python (additional experience in Go, Java, or Scala is a plus).
• Experience working with infrastructure-as-code tools such as Terraform or CloudFormation.
• Familiarity with observability tools such as CloudWatch, Prometheus, Grafana, Datadog, or centralized logging platforms.
• Experience managing GPU-based workloads and scaling training/inference systems.
• Familiarity with data infrastructure tools such as BigQuery and cloud-native data pipelines.
• Bonus: Experience supporting LLMs or generative AI pipelines, distributed training systems, feature stores (e.g., Feast), real-time inference systems, or ML governance frameworks.
• A mindset focused on automation, reliability, performance, and continuous improvement in fast-scaling environments.

How you work: 

• Driven by Impact: You deliver results that matter—prioritizing high-value work, meeting deadlines, and adapting quickly while keeping outcomes clear.
• Strategic & Customer-Centric: You anticipate risks and opportunities, connect decisions to long-term growth, and build trust through proactive insights.
• Curious & Growth-Oriented: You seek knowledge, ask sharp questions, and apply learnings fast—challenging the status quo with a mindset of improvement.
• Collaborative & Resilient: You thrive in change by staying resourceful, solution-focused, and positive—removing roadblocks, sharing insights, and keeping morale high.
• Accountable & Honest: You own your work, hold yourself and others to a high bar, and use transparent feedback to drive growth.
• Emotionally Intelligent: You build trust through empathy and collaboration, foster inclusion, and inspire others with grit, optimism, and integrity.

Our approach to compensation:

We take a market-based & data-driven approach to compensation. We leverage data from trusted third-party compensation sources to help us understand the market value of a role based on function, level, geographic location, and scope. We evaluate compensation bi-annually, including performance and market-related factors.

Our salaries are benchmarked against market Total Cash Compensation for the geographic location of our job posting. Compensation for some roles is structured as On Target Earnings (OTE = base + commission/variable) while for others it is structured as Salary only.

To comply with local legislation and ensure transparency, we share salary ranges on all job postings. Skills, experience and other factors help determine the final salary we offer which may vary from the original range posted. 

Additionally, all permanent team members are eligible to participate in various benefits plans as part of their overall compensation package.

Salary Range: 

$ 145,000 -165,000 CAD

#LI-Hybrid 

Who we are:

MasterClass is the streaming platform where the world’s best come together so anyone, anywhere, can access and be inspired by their knowledge and stories. We put you in the room with the creators, thinkers, makers and leaders who have changed the world, so that you can change yours.

Members get unprecedented access to 200+ instructors and classes across a wide variety of fields, including Arts & Entertainment, Business, Design & Style, Sports & Gaming, Writing and more. Step into Nas’ recording studio and Gordon Ramsay’s kitchen, and go behind the big screen with James Cameron. Design your career with Elaine Welteroth, get ready to win with Lewis Hamilton, perfect your pitch with Shonda Rhimes and discover your inner negotiator with Chris Voss.

We’re a remote-first workforce with collaborative work spaces in San Francisco and Kitchener, Ontario, and employees in several U.S. states. If you’re interested in joining a dynamic, culture-driving company—where learning invaluable
skills is all in a day’s work—we invite you to apply.

Snapshot of the Role:

We’re looking for a Staff Infrastructure  Engineer who operates as a technical leader and force multiplier across the platform. This is a senior individual contributor role for someone who designs systems that scale beyond a single team, sets technical direction, and elevates how infrastructure is built and operated across MasterClass.

You’ll lead and provide direction for  cloud infrastructure, developer platforms, video infrastructure, and emerging AI/ML infrastructure—partnering closely with product engineering, content production, data, and AI teams. This role is ideal for someone who combines deep systems expertise with strong software engineering, thrives in ambiguity, and takes ownership from strategy through execution.

This is not a people-management role, but it carries significant technical leadership responsibility.

What You Will Do:

• Set technical direction for core infrastructure systems, balancing reliability, scalability, security, and developer velocity.
• Design and lead implementation of complex, cross-team infrastructure initiatives (AWS, EKS, CI/CD, shared platforms).
• Build production-grade software to power infrastructure automation, tooling, and internal platforms.
• Drive org-wide improvements in observability, incident response, reliability engineering, and cost efficiency.
• Lead video infrastructure initiatives  to evolve video delivery and CDN strategies for global scale and performance.
• Partner with AI and Data teams to define and build GPU-powered infrastructure, model-serving platforms, and GenAI inference/training workflows.
• Act as a technical mentor and reviewer, raising the bar for infrastructure design and operational excellence across teams.
• Lead by example during incidents, postmortems, and critical architectural decisions.

About You (Requirements):

• 10+ years building and operating cloud-native production systems.
• Strong expertise in:
  • AWS architecture and large-scale cloud operations
  • Kubernetes / EKS (networking, RBAC, Helm, autoscaling, cluster-level debugging)
  • Infrastructure as Code (Terraform preferred)
  • CI/CD and automation (GitHub Actions, Argo, or similar)
• Proven ability to design and build infrastructure software, not just operate systems.
• Deep understanding of distributed systems, networking, Linux internals, and cloud security.
• A track record of driving step-function improvements in reliability, scalability, or developer experience.
• Strong written and verbal communication skills; able to influence technical decisions across teams.

Who You Are

• You think in systems and leverage, not just tasks.
• You care deeply about craftsmanship, clarity, and long-term maintainability.
• You enjoy solving ambiguous, cross-functional technical problems.
• You’re pragmatic—you optimize for impact over novelty.
• You raise the bar through mentorship, design reviews, and thoughtful collaboration.
• You’re energized by both strategic architecture and hands-on implementation.

Nice To Haves

• AI/ML infrastructure, GPU scheduling, or distributed training
• Model serving frameworks (e.g., Triton, vLLM, TensorRT-LLM)
• Infrastructure autoscaling strategies, including application workloads, databases, and GenAI workloads
• KEDA, Karpenter, service mesh, or multi-cluster architectures
• HLS/DASH packaging or video encoding pipelines
  
  

  Nice-to-haves are not required; each represents a direction in which our infrastructure is evolving.

At MasterClass, we believe we put our best work forward when our employees bring together ideas that are diverse in thought. We are proud to be an equal opportunity workplace and are committed to equal employment opportunity regardless of race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, genetic information, veteran status, gender identity or expression, sexual orientation, or any other characteristic protected by applicable federal, state or local law. In addition, MasterClass will provide reasonable accommodations for qualified individuals with disabilities.  If you have a disability or special need, we would like to know how we can better accommodate you.